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arXiv 2018年

作者： Xing, Frank Z. Cambria, Erik Malandri, Lorenzo Vercellis, Carlo School of Computer Science and Engineering Nanyang Technological University Data Mining and Optimization Research Group Politecnico di Milano

Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants' behavior is affected by public mood has been rarely discussed. Consequently, there has been little progress in leveraging public mood for the asset allocation problem, which is preferred in a trusted and interpretable way. In order to address the issue of incorporating public mood analyzed from social media, we propose to formalize public mood into market views, because market views can be integrated into the modern portfolio theory. In our framework, the optimal market views will maximize returns in each period with a Bayesian asset allocation model. We train two neural models to generate the market views, and benchmark the model performance on other popular asset allocation strategies. Our experimental results suggest that the formalization of market views significantly increases the profitability (5% to 10% annually) of the simulated portfolio at a given risk level. Copyright © 2018, The Authors. All rights reserved.

关键词： Investments

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A decision support system with distributed agents for large-scale process control

A decision support system with distributed agents for large-...

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Mediterranean Conference on Control and Automation (MED)

作者： A. Prayati A. Stathaki E. Furusjo R. E. King Data and Knowledge Engineering Group Computer Technology Institute Athens Greece Environmental Research Institute IVL Stockholm Sweden

As CIM systems are being developed to assist plant managers and operators with real-time monitoring and control, the need for decision support systems (DSS) becomes apparent so as to investigate alternative control strategies, fault diagnosis and alarming situations. As processes become more complex the need to consider also the environmental impact, on top of the commonly used economical factors, leads to new system architectures and multi-objective optimization. In this paper, a DSS architecture is presented considering distributed agents taking into account a multiplicity of impact factors in order to improve overall process operation.

关键词： Decision support systems Large-scale systems Process control Environmental economics Control systems Power generation economics Runtime Monitoring SCADA systems Knowledge management

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PraK Tool: An Interactive Search Tool Based on Video data Services 30th

PraK Tool: An Interactive Search Tool Based on Video Data S...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

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Using mapping languages for building legal knowledge graphs from XML files

Using mapping languages for building legal knowledge graphs ...

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2019 Blockchain Enabled Semantic Web Workshop and Contextualized Knowledge Graphs Workshop, BlockSW-CKG 2019

作者： Junior, Ademar Crotti Orlandi, Fabrizio O'Sullivan, Declan Dirschl, Christian Reul, Quentin ADAPT Centre for Digital Content Platform Research Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Wolters Kluwer Deutschland Munchen Germany Wolters Kluwer N.V. Chicago United States

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： XML

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Folkrank: A ranking algorithm for folksonomies

Folkrank: A ranking algorithm for folksonomies

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Workshop-Woche on Lernen, Wissen und Adaptivitat, LWA 2006

作者： Hotho, Andreas Jäschke, Robert Schmitz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D34121 Kassel Germany Research Center L3S Expo Plaza 1 D30539 Hannover Germany

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].

关键词： Semantic Web

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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Architecture proposal for adding FOG processing capability to a Smart Building Management System

Architecture proposal for adding FOG processing capability t...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： David Martínez Casas Andrea Rey Presas José M. Cotos Yáñez José A. Taboada González Electronics and Computer Science Department Computer Graphics and Data Engineering (COGRADE) Research Group University of Santiago de Compostela Santiago de Compostela Spain

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

This article examines the current state of the intelligent building monitoring system created for the University of Santiago de Compostela (USC) in the framework of the OPERE project and proposes a modification based on the Fog Computing paradigm. The study is developed in the context of the European regulations for the energy efficiency of facilities and the reduction of greenhouse gases. The current system implements the data processing in DADIS modules, developed by this research group for the acquisition and flexible transmission of information. These modules provide the information to the monitoring system which offers functionalities such as energy consumption dashboards, configurable operation schedules and ad hoc data visualization. However, the limitations of the current system include the difficulty in scaling the processing of the acquired information and database queries. The described upgrade proposes the extensive use of MQTT to standardize communications and allow the development of stand-alone applications to scale processing. The same architecture facilitates the incorporation of Big data infrastructures that would solve even more complex query problems than those addressed in this scenario.

关键词： Schedules Smart buildings Multi-access edge computing Architecture Europe computer architecture Regulation Proposals Greenhouse gases Monitoring

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Expansion microscopy allows quantitative characterisation of structural organisation of platelet aggregates

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Journal of Thrombosis and Haemostasis 2025年

作者： Emma L. Faulkner Jeremy A. Pike Evelyn Garlick Robert K. Neely Iain B. Styles Stephen P. Watson Natalie S. Poulter Steven G. Thomas Department of Cardiovascular Sciences School of Medical Sciences College of Medicine and Health University of Birmingham Birmingham UK COMPARE University of Birmingham and University of Nottingham Midlands UK Current address: Institute for Interdisciplinary Data Science and AI University of Birmingham Birmingham UK Research Software Group Advanced Research Computing University of Birmingham Birmingham UK Current address: Abberior Instruments GmbH Hans-Adolf-Krebs-Weg 637077 Göttingen Germany School of Chemistry University of Birmingham Birmingham UK School of Computer Science University of Birmingham Birmingham UK Current Address: School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast UK

Background Current microscopy approaches applied to platelet aggregates from multiple settings indicate their structure has important implications in efficient haemostasis and in clinical treatment of thrombosis. However, current fluorescence microscopy approaches are not amenable to detailed volumetric imaging of platelet aggregate structures. This is due to the small size of individual platelets and tight packing of platelets within aggregates, resulting in optical opacity. Objectives To address this, we investigated methods for performing super-resolution microscopy and image analysis on aggregates of platelets to describe their morphology. Methods We applied expansion microscopy and custom analysis workflows to extract quantitative information on the structure of platelet aggregates. Results We demonstrate that expansion microscopy applied to platelet aggregates, reveals multi-scale information about their structure. We produced volumetric images at nanoscale resolution of >700 platelet aggregates stained for cytoskeletal and membrane components under normal conditions and following cytoskeletal disruption. We demonstrate our custom analysis workflow provides quantitative description of platelet numbers, volumes and morphology within entire platelet aggregates. Additionally, we quantitatively describe subcellular organisation of F-actin. By comparing these measurements following treatment with the actin inhibitors, cytochalasin D and latrunculin A, we can robustly detect structural disruptions in platelet aggregates. Conclusions Together these data provide a workflow to qualitatively and quantitatively describe the architecture of platelet aggregates at a range of scales (whole aggregates down to sub-cellular features within individual platelets). This provides an important tool for analysis of platelet aggregates and thrombi in conditions such as haemostasis, immunothrombosis and cardiovascular disease.

关键词： F-actin platelet aggregation microscopy

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